The development and installation of downhole and surface sensors to measure pressures, temperatures, voltages, etc. requires methods to process and interpret gigabytes of continuous data streams. The introduction of permanent sensor technologies allows data to be collected continuously at high frequencies over long periods of time, resulting in the generation of gigabytes of data that become difficult and time consuming to interpret on a continuous basis. The practice, therefore, has been either to reduce the frequency of acquisition to get manageable data sets or to access and interpret data subsets only where a known transient is introduced, such as a well test. Under these conventional methods, the full value of the sensors is not realized because interesting regions in the data may be missed. These regions may contain significant information about the reservoir and wellbore.
Athichanagorn, Horne and Kikani disclose a wavelet technique to identify transients in continuous data streams in “Processing and Interpretation of Long Term Data from Permanent Downhole Pressure Gauges”, SPE Annual Technical Conference and Exhibition, Houston, Tex., Oct. 3-6, 1999, SPE56419 (incorporated by reference herein in its entirety). However, this method analyzes the entire data set at specific time scales. The time scales are chosen by the specific wavelet transform and are independent of sensor physics or the objective of measurement interpretation. Athichanagorn et al. also use a preprocessor to filter out the noise that could erroneously also remove sharp low amplitude transients, which may be relevant for reservoir evaluation. The data processing algorithms used in accordance with the present invention generates a few relevant subsets using relevant criteria and, preferably, at a few time scales. The algorithms are flexible and relevant criteria used to develop the data subsets can be adjusted over the lifetime of the reservoir. These algorithms work on raw signal data and require no preprocessing or filtering. Moreover, they generate compressed data sets as outputs that can be tailored for different end users.
In conventional methods, data interpretation usually involves history matching with full-field reservoir simulators. This could take months. In real-time reservoir management, it is preferable to take corrective measures at much faster time scales.
Accordingly, it is an object of the present invention to allow for efficient data processing and data interpretation for real-time monitoring/reservoir evaluation.
It is another object of the present invention to provide tools and methods for processing and interpreting these vast sets of data so as to extract all the useful information in the most efficient way. These tools work both when data streams arrive continuously as well as when the archived database is accessed periodically.
It is yet another object of the present invention to provide interpretation methodologies at varying levels of detail, ranging from quick look interpretations over a time scale of days to detailed modeling over a time scale of months, so that the information from the sensors can be used effectively and their full benefits realized.